Clustering Local L-1 Maxima¶
In the de novo mode analysis, after the local maxima have been identified from the tissue image, they are clustered.
The default clustering algorithm is based on Louvain community
detection.
SSAM also supports clustering using hdbscan
and optics
.
It can be initiated by:
analysis.cluster_vectors(method="louvain",
pca_dims=-1,
min_cluster_size=2,
max_correlation=1.0,
metric="correlation",
outlier_detection_method='medoid-correlation',
outlier_detection_kwargs={},
random_state=0,
**kwargs)
… where - method
can be louvain
, hdbscan
, optics
. -
pca_dims
are the number of principal componants used for clustering.
- min_cluster_size
is the minimum cluster size. - resolution
is
the resolution for Louvain community detection. - prune
is the
threshold for Jaccard index (weight of SNN network). If it is smaller
than prune, it is set to zero. - snn_neighbors
is the number of
neighbors for SNN network. - max_correlation
is the threshold for
which clusters with higher correlation to this value will be merged. -
metric
is the metric for calculation of distance between vectors in
gene expression space. - subclustering
if set to True, each cluster
will be clustered once again with DBSCAN algorithm to find more
subclusters. - dbscan_eps
is the eps
value for DBSCAN
subclustering. Not used when ‘subclustering’ is set False. -
centroid_correction_threshold
is the threshold for which centroid
will be recalculated with the vectors which have the correlation to the
cluster medoid equal or higher than this value. - random_state
is
the random seed or scikit-learn’s random state object to replicate the
same result
Removing outliers¶
The cell type signature is determined as the centroid of the cluster. This can be affected by outliers, so SSAM supports a number of outlier removal methods:
analysis.remove_outliers(outlier_detection_method='medoid-correlation', outlier_detection_kwargs={}, normalize=True)
robust-covariance
, one-class-svm
, isolation-forest
,
local-outlier-factor
- outlier_detection_kwargs
are arguments
passed to the outlier detection method